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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance Engineering	10-15%	- Warehouse Management 1. Multi-cluster warehouse configuration 2. Auto-suspend and auto-resume settings 3. Resource monitors and cost control - Query Optimization 1. Query rewrite and optimization techniques 2. Join elimination and simplification 3. Predicate pushdown optimization
Topic 2: Data Ingestion	15-20%	- Continuous Data Ingestion 1. Real-time data ingestion patterns 2. Snowpipe configuration and usage 3. Serverless compute for Snowpipe - Bulk Data Loading 1. Staging configurations (internal and external stages) 2. COPY INTO command options and best practices 3. Data loading error handling and validation 4. File format considerations (CSV, JSON, Parquet, Avro)
Topic 3: Advanced Snowflake Features	10-15%	- External Integrations 1. External functions and UDFs 2. Partner integrations (Spark, Kafka, etc.) 3. Snowflake Connector for Python/Java - Data Lakes 1. Snowflake as a Data Lake 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 3. External tables and Iceberg support - Data Sharing 1. Secure share creation and management 2. Direct sharing vs Data Marketplace 3. Reader accounts
Topic 4: Data Governance and Security	15-20%	- Access Control 1. Row-level and column-level security 2. GRANT and REVOKE operations 3. Role hierarchy and ownership 4. Role-based access control (RBAC) - Data Protection 1. External tokenization 2. Data masking and tokenization 3. Data re-identification risks 4. Secure data sharing across accounts - Compliance and Monitoring 1. Tag-based policies 2. Access history and audit trails 3. Data classification
Topic 5: Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Hybrid and Hyper tables 2. Micro-partitioning concepts 3. Table types (permanent, transient, temporary) 4. Clustering strategies and key selection - Performance Optimization 1. Materialized views and cached results 2. Warehouse scaling policies 3. Result set caching 4. Query profiling and execution plans
Topic 6: Data Transformation	20-25%	- SQL-Based Transformations 1. Window functions for advanced analytics 2. Complex JOIN operations and optimizations 3. Data type conversions and casting 4. Merge, update, and delete operations - Data Processing Patterns 1. ELT vs ETL approaches in Snowflake 2. Zero Copy Cloning for data provisioning 3. Time Travel for historical data access - Streams and Tasks 1. Scheduled and event-based task execution 2. Change Data Capture (CDC) with streams 3. Task graph design and dependencies 4. Serverless task warehouse configuration

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q94-Q99):

NEW QUESTION # 94
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
B. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
C. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
D. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Answer: A

NEW QUESTION # 95
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)

A. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
B. Use Amazon Redshift ML to generate inventory recommendations.
C. Use Amazon Redshift ML to schedule regular data exports for offline model training.
D. Use SQL to invoke a remote SageMaker endpoint for prediction.
E. Use Amazon Redshift as a file storage system to archive old inventory management reports.

Answer: B,D

Explanation:
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift.
Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.

NEW QUESTION # 96
A company has as JSON file that contains personally identifiable information (PII) data and non- PII data. The company needs to make the data available for querying and analysis.
The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.
B. Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.
C. Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.
D. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation.
Use Lake Formation permissions to provide access to the required data based on the type of user.

Answer: D

Explanation:
AWS Lake Formation lets you register your JSON file as a table in the data catalog and apply fine-grained permissions (down to columns or rows) without moving or transforming the underlying data. You can grant all employees access to the non-PII columns while restricting the PII columns to a small group, achieving your security requirements with minimal operational overhead.

NEW QUESTION # 97
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?

A. Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.
B. Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.
C. Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.
D. Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.

Answer: C

Explanation:
Amazon Redshift's dynamic data masking lets you define masking policies on sensitive columns without duplicating data. You attach a masking policy that exposes only the last four characters to customer support IAM roles, while audit IAM roles see the full values. This meets the requirement for role-based column-level access with no data duplication.

NEW QUESTION # 98
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?

A. FALSE
B. TRUE

Answer: B

Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.

NEW QUESTION # 99
......

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